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Getting the right exposure is a critical ingredient in taking a good photo. And while your camera’s Auto mode does a pretty good job most of the time, there’s often room for improvement. Thankfully, tweaking the exposure is not as complicated as it might seem—after you learn a few simple rules, you can ensure that your photos pop with color and energy.

A well-exposed photo is neither under- nor over-exposed. That might sound obvious, but in practical terms, what does it mean?

In an underexposed photo, some parts of the image didn’t receive enough light, and so they appear as pure black. Nothing can fix these parts of the image—they received no light, and no amount of post-processing can restore detail to the photo, since none was captured to begin with.

An overexposed photo has the opposite problem. Photographers often refer to an overexposed photo as “blown out,” because it received so much light that all detail was blown away. Like an underexposed photo, you can’t salvage blown out areas—they are pure white, and fiddling with the photo will only make those areas various shades of grey.

There’s a fairly straightforward way to keep all three of these rules in check using a little tool called a histogram. Almost every camera, from the compact point and shoot to the professional Digital SLR, contains a histogram mode in the LCD display. (Not all cameras do, though—if you have a very simple point and shoot or a smartphone, for example, you’re probably going to be left out in the cold.) If you’ve never seen yours, check the camera’s user guide to see how to turn it on.

When enabled, you’ll see what looks like a graph in the display when you review photos you’ve just taken. That means you can quickly check your shots as you take them to make sure they’re properly exposed, and reshoot if necessary—even though LCD camera displays are too small to really see photographic details.

When there are no sudden spikes at the extreme edges of a histogram, the photo is neither under- nor over-exposed.

Even if you never liked math class and feel intimidated by charts and graphs, fear not: The histogram is pretty easy to read. The X-axis, which goes from right to left, shows the range of exposure from shadow to highlight. Each bar in the chart shows the relative number of pixels. So if the graph is high on the left side, that means there are a lot of dark pixels in the photo. It’s probably somewhat underexposed, or perhaps it just means there’s a lot of shadows or dark objects in the scene. If the graph is high on the right, the opposite is true: the scene is probably very bright.

In this scene, the camera tried to capture both a bright sky and dark shadows. The shadows turned out pure black, devoid of detail.

Throughout this article, I’ll show you histograms like this one. These are taken from Adobe Photoshop, but in principle they look quite similar to the one on the back of your camera.

Don’t assume that the histogram should have a certain shape to be properly exposed; you should just ensure that graph isn’t pegged at the extreme left or right edges. The exact shape of the graph isn’t especially important. It doesn’t matter if you have a classic bell curve, for example, or a chart that has random spikes in it; there’s nothing necessarily wrong with the exposure.

It’s easy to accidentally overexpose things like the sky, beach sand, and snow. A peek at the right edge of the histogram will reveal if the photo can be salvaged.

Problems happen when the histogram spikes at either edge of the chart. If the histogram is pegged at the extreme left edge of the chart, that means you have a lot of pixels with a value of zero—pure black—in the photo. Pure black is usually bad, because that means the camera recorded no detail in those parts of the photo. If you see a histogram that looks like this, your best bet is to take the photo again—this time, increase the exposure a bit, perhaps by setting the Exposure Compensation control to +1 or +2.

It should not be a great surprise to see a night exposure underexposed. The inky black, silhouetted trees are responsible for the cluster of pure black pixels.

You can easily get a histogram with the opposite problem as well. If there’s a spike of pixels at the extreme right side of the chart, which corresponds to a brightness of 255, then you have a lot of pure white pixels. These pixels have no information either. So unless you’re taking a photo of a white piece of paper, that’s probably not what you’re looking for.

It’s worth pointing out that your artistic judgment as a photographer can still trump the histogram. You might intentionally want underexposure in a photo, for example. If you’re trying to capture a silhouette against the setting sun, for example, or you’re shooting a photo at night, a cluster of pure black pixels might not just unavoidable, they might be exactly what you’re looking for. Always rely on your own judgment.

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